电力系统自动化2025，Vol.49Issue(21)：120-130,11.DOI:10.7500/AEPS20241105007
基于混合特征提取和机器学习的锂离子电池健康状态估计
State of Health Estimation for Lithium-ion Batteries Based on Hybrid Feature Extraction and Machine Learning
摘要
Abstract
Accurately estimating the state of health(SOH)of lithium-ion batteries is crucial for promoting the development of electric vehicles.However,the complex battery aging mechanism remains a major challenge.Therefore,an SOH estimation model based on hybrid feature extraction and machine learning is proposed.Firstly,based on incremental capacity analysis(ICA)and Spearman correlation analysis,features are extracted from the incremental capacity(IC)curve after sliding average filtering.Secondly,features are extracted from two visual representations of electrochemical impedance spectroscopy,namely Nyquist plot and Bode plot.Then,the k-means clustering method is used to streamline the above features,and an SOH estimation model is established for the CNN-BiGRU-Attention network based on Bayesian optimization.Furthermore,one-dimensional convolution in the network is used to excavate the deep-level information in the original features.And the bi-directional gated recurrent unit(BiGRU)network with multi-head attention mechanism can capture the key information containing battery aging in the input sequence more effectively to estimate SOH of batteries.Finally,experiments are conducted using battery datasets with three different electrode materials to validate the effectiveness of the proposed method.关键词
锂离子电池/健康状态/特征提取/机器学习/增量容量分析/卷积神经网络(CNN)/双向门控循环单元(BiGRU)/多头注意力机制Key words
lithium-ion battery/state of health(SOH)/feature extraction/machine learning/incremental capacity analysis/convolutional neural network(CNN)/bi-directional gated recurrent unit(BiGRU)/multi-head attention mechanism引用本文复制引用
谢国民,刘澳..基于混合特征提取和机器学习的锂离子电池健康状态估计[J].电力系统自动化,2025,49(21):120-130,11.基金项目
国家自然科学基金资助项目(51974151) （51974151）
辽宁省教育厅基础项目(LJKMZ20220683). This work is supported by National Natural Science Foundation of China(No.51974151)and Basic Project of Liaoning Provincial Department of Education(No.LJKMZ20220683). （LJKMZ20220683）
